Improving Top-N recommendations with user consuming profiles

Ren, Yongli, Li, Gang and Zhou, Wanlei 2012, Improving Top-N recommendations with user consuming profiles, in PRICAI 2012 : Trends in Artificial Intelligence : 12th Pacific Rim International Conference on Artificial Intelligence, Kuching, Malaysia September 3-7 2012 : proceedings, Springer-Verlag, Berlin, Germany, pp. 887-890.

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Title Improving Top-N recommendations with user consuming profiles
Author(s) Ren, Yongli
Li, Gang
Zhou, Wanlei
Conference name Pacific Rim International Conference on Artificial Intelligence (12th : 2012 : Kuching, Malaysia)
Conference location Kuching, Malaysia
Conference dates 3-7 Sept. 2012
Title of proceedings PRICAI 2012 : Trends in Artificial Intelligence : 12th Pacific Rim International Conference on Artificial Intelligence, Kuching, Malaysia September 3-7 2012 : proceedings
Editor(s) Anthony, Patricia
Ishizuka, Mitsuru
Lukose, Dickson
Publication date 2012
Series Lecture Notes in Computer Science ; v.7458
Conference series Pacific Rim International Conference on Artificial Intelligence
Start page 887
End page 890
Total pages 4
Publisher Springer-Verlag
Place of publication Berlin, Germany
Summary In this work, we observe that user consuming styles tend to change regularly following some profiles. Therefore, we propose a consuming profile model to capture the user consuming styles, then apply it to improve the Top-N recommendation. The basic idea is to model user consuming styles by constructing a representative subspace. Then, a set of candidate items can be estimated by measuring its reconstruction error from its projection on the representative subspace. The experiment results show that the proposed model can improve the accuracy of Top-N recommendations much better than the state-of-the-art algorithms.
ISBN 9783642326943
ISSN 0302-9743
Language eng
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30051351

Document type: Conference Paper
Collection: School of Information Technology
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